The Mathematics and Computer Science Division (MCS) at Argonne National Laboratory is seeking a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning, focusing specifically on developing machine learning-based surrogates and emulators for the dynamics of power grids.
Requirements
- Strong proficiency in Python, with additional experience in C, C++, or similar languages
- Demonstrated expertise in machine learning, especially in the context of dynamical systems modeled by differential-algebraic equations
- Experience with high-performance computing and the ability to scale models using distributed computing environments
- Extensive experience with power grid models and large-scale optimization problems
- Familiarity with developing machine learning surrogates and emulators for dynamical systems
- Proficiency in managing large datasets and training with GPU-enabled computing resources
- Expertise in numerical optimization and familiarity with ML frameworks such as PyTorch, Jax, or TensorFlow
Responsibilities
- creating advanced probabilistic models that capture the complex behaviors of dynamical systems
- integrating machine learning models into large-scale optimization frameworks to enhance the efficiency and reliability of power grid operations
- conceptual framework, design, and implementation of machine learning models
- ensuring trustworthy computations and scalability on the DOE’s leadership computing facilities
- developing robust, scalable solutions that are computationally efficient and maintain accuracy within the operational constraints of real-world power systems
Other
- Ph.D. (completed within the past 0-5 years) in computer science, electrical engineering, applied mathematics, or a related field
- Excellent oral and written communication skills for effective collaboration across multiple teams
- Commitment to embodying the core values of impact, safety, respect, and teamwork in all endeavors
- Background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis
- Government access authorization that involves additional background check requirements
